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Record W2092390956 · doi:10.1021/ac1013157

In-Gel Technology for PCR Genotyping and Pathogen Detection

2010· article· en· W2092390956 on OpenAlexafffund
Alexey Atrazhev, Dammika P. Manage, Alexander J. Stickel, H. John Crabtree, Linda M. Pilarski, Jason P. Acker

Bibliographic record

VenueAnalytical Chemistry · 2010
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsCanadian Blood ServicesUniversity of Alberta
FundersCanada Research Chairs
KeywordsChemistrygenomic DNAChromatographyGel electrophoresisPolymerase chain reactionSYBR Green IMolecular biologyDNAPolymerizationPrimer (cosmetics)GeneBiologyBiochemistryPolymer

Abstract

fetched live from OpenAlex

This work describes the use of polyacrylamide gel and PCR reagents photopolymerized in a mold to create an array of semisolid posts that serve as reaction vessels for parallel PCR amplification of an externally added template. DNA amplification occurred in a cylindrical, self-standing 9 × 9 array of gel posts each less than 1 μL in volume. Photopolymerization of the gel with an intercalating dye added prior to polymerization permitted acquisition of real-time PCR data and melting curve analysis data without the need for any type of post-PCR staining procedures. PCR was equally efficient and reproducible when template DNA was polymerized within the gel or when exogenous template was added atop precast gel posts. PCR amplification occurred with template from purified DNA or from raw urine of patients with BK viruria. Multiple primer sets can be utilized per gel post array with no detectable cross contamination. As few as 34 BK virus templates were consistently detected by PCR in an individual gel post. Amplification of HPA1 and FGFR2 genes in human genomic DNA (gDNA) required as little as 2-5 ng of gDNA template/gel post. The device prototype includes a Peltier element for PCR thermal cycling and a CCD camera to capture fluorescence for product detection. Our technology is amenable to integration in point of care microdevices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.270
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2010
Admission routes2
Has abstractyes

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